To verify the effectiveness of our proposed method DeepSCC, we choose a corpus collected from Stack Overflow, which contains 224,445 pairs of code snippets and corresponding language types. We first perform data preprocessing on this corpus, such as word segmentation, discarding noisy code snippets. Then, we use the corpus to fine-tuning the RoBERTa model [1]}. We compared DeepSCC with nine state-of-the-art baselines. For these chosen baselines, two baselines are selected from the source code classification field [2]}, [3]}, and the remaining baselines are selected from the neural text classification field [4]}, [5]}, [6]}, [7]}, [8]}.
In terms of four performance measures (Accuracy, Precision, Recall, and F1), we find DeepSCC can outperform these baselines.
